I am a student in GIS I have a dissertation about land use change. I have questions about using ArcMap 10 for this aim, how can using GIS to detect the percentage of land use change. For example we want to identify land use change in Sheffield city between 1990 until 2000
Land use of Urban %30 1990 also it is changed to %40 in 2000 how can detect this result I mean (%30 and %40).

2 Answers
2

there are lots of method for calculating land use change in gis systems. if you want to use Arcgis, you should check out Confusion Matrix Analysis for your work. with confusion matrix you can also measure urban sprawl, too.. it is of course necessary to do some research.

In the field of artificial intelligence, a confusion matrix is a
specific table layout that allows visualization of the performance of
an algorithm, typically a supervised learning one (in unsupervised
learning it is usually called a matching matrix). Each column of the
matrix represents the instances in a predicted class, while each row
represents the instances in an actual class. The name stems from the
fact that it makes it easy to see if the system is confusing two
classes (i.e. commonly mislabeling one as another). Outside artificial
intelligence, the confusion matrix is often called the contingency
table or the error matrix.

there is good source about Land Use Map Comparison and Accuracy Analysis, here.

Error Matrix for Map Comparison or Accuracy Assessment with well documented workflow, here.

As Aragon correctly points out, there are many options for detecting changes in land use using GIS.

Before starting, it's worth being aware of the differences between land USE and land COVER, see here for a helpful explanation (it's certainly a distinction that you'll want to make in your dissertation). In addition, bare in mind that any data that you collect will represent a snapshot of land use/cover at that particular time point and does therefore not necessarily represent trend of changing land use or cover (this is less important for urban studies but is particular poignant for rural studies of land use change where land management practices, such as arable cropping regimes, may frequently change).

A few methods might be (in order of sophistication):

Get hold of two images (one for each year), perhaps aerial photographs, and delineate land parcels yourself (simply by digitising each of the land uses in turn, calculating polygon areas and comparing the two maps. The obvious caveat being that this is a very subjective approach.

Get hold of land registry data from your friendly Local Authority for the two time periods and associate this information with individual land parcels (OS MasterMap might be useful for this which you can request for a nominal fee as a student from the Ordnance Survey). Bare in mind that OS MasterMap is a modern-day representation of land use and some polygon editing will inevitably be required to tie the two sets of information together.

Use historical aerial photographs or satellite data (perhaps gathered from your friendly Local Authority contact) and use a GIS like ArcGIS (or better yet, in my opinion, IDRISI's Land Change Modeller suite) to classify the two images based on their spectral signatures (a simple Google of 'supervised classification' yields plenty of results - here), train the model and incorporate some form of validation procedure whereby your model is able to recreate a land use/cover at a given time period (perhaps using LCM2007 as your validation dataset). You could even project land use beyond the present day using a cellular-automata or Markov model (see my PhD thesis, and associated references, here). Depending upon the level of your degree, and your ability/familiarisation with GIS, this approach may be beyond your remit.

The above should get you started. Also be aware that in the UK we have Land Cover Map 2000 and now LCM2007 (for a useful article on LCM2000 - see here) which, as a student, you will have access to via your university. This dataset might provide a useful starting point but bare in mind it's potential limitations for your study (it is raster based, represents 25m x 25m cells which may be too coarse, depicts land cover and not land USE etc) and the fact that it cannot be compared to the earlier (LCM1990) dataset due to differences in sampling methods.